One of the holy grails of computer science is the development of an AI that can extrapolate from data. A team of researchers from the University of Southern California has announced the development of something profoundly new — a model for an AI with imagination.
Understanding “why” may be the key to unlocking an AI’s imagination.
Scientists hunting for elusive gravitational waves across the universe may be able to supercharge their discoveries with a new tool: artificial intelligence.
Gravitational waves are ripples in spacetime, created when a massive object is accelerated or disturbed, such as when a black hole and a neutron star collide. Theorized by Albert Einstein, their existence was confirmed in 2015 with the first gravitational wave discovery by researchers using LIGO (the advanced Laser Interferometer Gravitational-Wave Observatory). Now, just six years later, there have been at least 50 gravitational wave events detected.
A program has been used to predict the structures of nearly every protein in the human body.
The company has already used AlphaFold, its protein folding AI, to generate structures for nearly all of the human proteome, as well as yeast, fruit flies, mice and more.
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With help from Microsoft’s artificial intelligence systems, the BeachBot learns how to better find the strewn filters, even if they’re partially buried in the sand. It then scoops these cigarette butts up from the sand and disposes of them in an internal bin. Later, people empty that bin into a trash container. Rolling atop the sand on four puffy-looking wheels, the beach-cleaning robot uses two onboard cameras to look ahead (to avoid people and objects) and to look down.
The BeachBot is still in early learning via the software giant’s Trove AI system, which helps provide image sets for this kind of machine learning task. Teaching the bot how to find its prey requires a lot of people. TechTics must show the beach rover (and, specifically, the AI system) thousands of photos of cigarette butts, all lying about in various states, such as partially hidden, so it can recognize and remember them.
Continue reading “AI-enabled BeachBot robot to clean up cigarette butts on beaches” »
A new wearable brain-machine interface (BMI) system could improve the quality of life for people with motor dysfunction or paralysis, even those struggling with locked-in syndrome—when a person is fully conscious but unable to move or communicate.
A multi-institutional, international team of researchers led by the lab of Woon-Hong Yeo at the Georgia Institute of Technology combined wireless soft scalp electronics and virtual reality in a BMI system that allows the user to imagine an action and wirelessly control a wheelchair or robotic arm.
The team, which included researchers from the University of Kent (United Kingdom) and Yonsei University (Republic of Korea), describes the new motor imagery-based BMI system this month in the journal Advanced Science.
The prediction of protein structures from amino acid sequence information alone, known as the “protein folding problem,” has been an important open research question for more than 50 years. In the fall of 2020, DeepMind’s neural network model AlphaFold took a huge leap forward in solving this problem, outperforming some 100 other teams in the Critical Assessment of Structure Prediction (CASP) challenge, regarded as the gold-standard accuracy assessment for protein structure prediction. The success of the novel approach is considered a milestone in protein structure prediction.
This week, the DeepMind paper Highly Accurate Protein Structure Prediction with AlphaFold was published in the prestigious scientific journal Nature. The paper introduces AlphaFold2, a completely redesigned and open-sourced model that can predict protein structures with atomic-level accuracy.
Although machine learning researchers have long sought to develop computational methods for predicting 3D protein structures from protein sequences, there had been limited progress along this path, chiefly due to the computational intractability of molecular simulation, the context-dependence of protein stability, and the difficulty of producing sufficiently accurate models for protein physics.
BlueOcean, a startup leveraging AI and machine learning to measure brand sentiment, has raised $15 million in capital.
